Overview

Dataset statistics

Number of variables6
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.0 KiB
Average record size in memory48.1 B

Variable types

NUM6

Reproduction

Analysis started2020-08-25 00:25:53.479785
Analysis finished2020-08-25 00:26:00.160962
Duration6.68 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.0861549526453018e-09
Minimum-2.376112937927246
Maximum2.2394869327545166
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:26:00.207942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.376112938
5-th percentile-1.609716463
Q1-0.7472357154
median-0.01212761831
Q30.774248898
95-th percentile1.622720724
Maximum2.239486933
Range4.615599871
Interquartile range (IQR)1.521484613

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)-920678949
Kurtosis-0.795288708
Mean-1.086154953e-09
Median Absolute Deviation (MAD)0.7733484907
Skewness-0.03169212465
Sum-1.086154953e-06
Variance1.000000001
2020-08-25T00:26:00.311417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.50390982610.1%
 
0.939378619210.1%
 
0.875675141810.1%
 
-1.27478516110.1%
 
0.940125882610.1%
 
0.207351505810.1%
 
-1.58728015410.1%
 
-0.725279748410.1%
 
-0.860043883310.1%
 
0.454744279410.1%
 
1.62242567510.1%
 
-1.78648304910.1%
 
-0.901049017910.1%
 
2.15888690910.1%
 
-0.846360921910.1%
 
1.03775525110.1%
 
-0.0277297440910.1%
 
0.762371778510.1%
 
-0.0120340548510.1%
 
0.341957181710.1%
 
1.97004246710.1%
 
1.5885561710.1%
 
1.19012033910.1%
 
-1.42315876510.1%
 
0.440750539310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.37611293810.1%
 
-2.28813934310.1%
 
-2.26750326210.1%
 
-2.25151014310.1%
 
-2.11430978810.1%
 
-2.09441518810.1%
 
-2.08913230910.1%
 
-2.06238508210.1%
 
-2.03884649310.1%
 
-2.02227401710.1%
 
ValueCountFrequency (%) 
2.23948693310.1%
 
2.21708154710.1%
 
2.1880366810.1%
 
2.15888690910.1%
 
2.14342570310.1%
 
2.10940313310.1%
 
2.08746552510.1%
 
2.07287359210.1%
 
2.02212667510.1%
 
1.9873812210.1%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.0104849934577942e-10
Minimum-1.7677372694015503
Maximum1.6761734485626218
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:26:00.427983image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.767737269
5-th percentile-1.608326346
Q1-0.8264390081
median0.06779623777
Q30.8584127575
95-th percentile1.534630281
Maximum1.676173449
Range3.443910718
Interquartile range (IQR)1.684851766

Descriptive statistics

Standard deviation0.9999999999
Coefficient of variation (CV)-9896238009
Kurtosis-1.192371052
Mean-1.010484993e-10
Median Absolute Deviation (MAD)0.8400177434
Skewness-0.0644953463
Sum-1.010484993e-07
Variance0.9999999999
2020-08-25T00:26:00.547454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.738992035410.1%
 
-1.40760028410.1%
 
0.0653428956910.1%
 
-1.62638628510.1%
 
1.12247645910.1%
 
-1.0189945710.1%
 
0.420266509110.1%
 
1.15411782310.1%
 
-0.778029620610.1%
 
-0.0242523737310.1%
 
-0.0794311985410.1%
 
-0.1078247810.1%
 
1.22792780410.1%
 
0.940727829910.1%
 
-0.938177168410.1%
 
-0.641298592110.1%
 
-0.869777381410.1%
 
0.750669002510.1%
 
0.297208547610.1%
 
-1.3813154710.1%
 
1.09508049510.1%
 
-1.4322700510.1%
 
-0.841949999310.1%
 
1.66538095510.1%
 
-1.61068022310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.76773726910.1%
 
-1.7604403510.1%
 
-1.75297939810.1%
 
-1.74359178510.1%
 
-1.73837935910.1%
 
-1.73827040210.1%
 
-1.73684084410.1%
 
-1.73437510.1%
 
-1.73426127410.1%
 
-1.72770416710.1%
 
ValueCountFrequency (%) 
1.67617344910.1%
 
1.67409694210.1%
 
1.66538095510.1%
 
1.6640082610.1%
 
1.65714776510.1%
 
1.65343105810.1%
 
1.6528593310.1%
 
1.65073740510.1%
 
1.64422643210.1%
 
1.64351034210.1%
 

oz3
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1521624401211739e-09
Minimum-2.1019771099090576
Maximum3.51170015335083
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:26:00.659722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.10197711
5-th percentile-1.374571037
Q1-0.7357462496
median-0.1660492122
Q30.5896431655
95-th percentile1.855903208
Maximum3.511700153
Range5.613677263
Interquartile range (IQR)1.325389415

Descriptive statistics

Standard deviation1
Coefficient of variation (CV)867933171.1
Kurtosis0.2123575418
Mean1.15216244e-09
Median Absolute Deviation (MAD)0.6325228214
Skewness0.6665959505
Sum1.15216244e-06
Variance1.000000001
2020-08-25T00:26:00.761462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.209472417810.1%
 
-1.01693415610.1%
 
-0.0784371420710.1%
 
-0.760286450410.1%
 
-0.766307413610.1%
 
-0.244799166910.1%
 
1.00917065110.1%
 
1.15880346310.1%
 
-0.00264239008510.1%
 
0.907031476510.1%
 
0.232750222110.1%
 
0.752616286310.1%
 
-0.282557606710.1%
 
-0.65886467710.1%
 
0.253654420410.1%
 
0.553388774410.1%
 
-0.247888907810.1%
 
-1.56771314110.1%
 
-0.598309159310.1%
 
-0.338108837610.1%
 
-0.9633135210.1%
 
0.0162556581210.1%
 
-0.434934049810.1%
 
1.63019752510.1%
 
0.455024361610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.1019771110.1%
 
-2.09398722610.1%
 
-2.01801395410.1%
 
-2.00047016110.1%
 
-1.955835710.1%
 
-1.91555929210.1%
 
-1.87875509310.1%
 
-1.84283852610.1%
 
-1.82907795910.1%
 
-1.78129720710.1%
 
ValueCountFrequency (%) 
3.51170015310.1%
 
3.40406870810.1%
 
3.38274025910.1%
 
3.36848068210.1%
 
3.14564561810.1%
 
3.08799648310.1%
 
2.84695410710.1%
 
2.79461908310.1%
 
2.75338244410.1%
 
2.67549896210.1%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2264506444334985e-10
Minimum-1.788364052772522
Maximum1.7906904220581057
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:26:00.869452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.788364053
5-th percentile-1.566818464
Q1-0.8277476281
median-0.04224487953
Q30.8556604236
95-th percentile1.607122946
Maximum1.790690422
Range3.579054475
Interquartile range (IQR)1.683408052

Descriptive statistics

Standard deviation0.9999999993
Coefficient of variation (CV)3099381052
Kurtosis-1.1111353
Mean3.226450644e-10
Median Absolute Deviation (MAD)0.8429518621
Skewness0.01764358149
Sum3.226450644e-07
Variance0.9999999986
2020-08-25T00:26:00.981186image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.34589004510.1%
 
0.0520440526310.1%
 
-0.398104697510.1%
 
-1.76401972810.1%
 
-0.347028851510.1%
 
-0.39976239210.1%
 
-1.33341705810.1%
 
0.0496037825910.1%
 
-0.202320933310.1%
 
1.50527715710.1%
 
-0.738965511310.1%
 
-0.0752807334110.1%
 
-0.801459729710.1%
 
-0.450534522510.1%
 
-0.524715244810.1%
 
-0.74871987110.1%
 
-0.709624767310.1%
 
0.787779927310.1%
 
-0.153172269510.1%
 
-1.21618056310.1%
 
-0.835247755110.1%
 
-0.19450095310.1%
 
-1.73178327110.1%
 
-0.735028743710.1%
 
1.12414312410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.78836405310.1%
 
-1.7734420310.1%
 
-1.77268922310.1%
 
-1.76494574510.1%
 
-1.76454734810.1%
 
-1.76401972810.1%
 
-1.76381766810.1%
 
-1.76280045510.1%
 
-1.76247084110.1%
 
-1.75674295410.1%
 
ValueCountFrequency (%) 
1.79069042210.1%
 
1.78602087510.1%
 
1.78402066210.1%
 
1.78151845910.1%
 
1.77782118310.1%
 
1.76391422710.1%
 
1.75416064310.1%
 
1.75116789310.1%
 
1.74494028110.1%
 
1.74315595610.1%
 

oz5
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.944173946976662e-11
Minimum-1.6825108528137207
Maximum1.7417236566543581
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:26:01.097872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.682510853
5-th percentile-1.530029362
Q1-0.8641477525
median-0.04965801351
Q30.8841769844
95-th percentile1.582783407
Maximum1.741723657
Range3.424234509
Interquartile range (IQR)1.748324737

Descriptive statistics

Standard deviation0.999999999
Coefficient of variation (CV)-1.440056091e+10
Kurtosis-1.214314239
Mean-6.944173947e-11
Median Absolute Deviation (MAD)0.8691505492
Skewness0.06718766939
Sum-6.944173947e-08
Variance0.999999998
2020-08-25T00:26:01.201284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.473631650210.1%
 
0.0746910050510.1%
 
1.17048835810.1%
 
-0.135426655410.1%
 
-0.60225284110.1%
 
-1.0990251310.1%
 
-0.0494809187910.1%
 
1.33848476410.1%
 
1.3919757610.1%
 
0.0655135810410.1%
 
0.00645178789310.1%
 
-1.0599324710.1%
 
0.477872997510.1%
 
-1.65756070610.1%
 
-0.246179565810.1%
 
0.78189480310.1%
 
-1.00270354710.1%
 
0.103595800710.1%
 
1.34247088410.1%
 
1.72783780110.1%
 
1.53251934110.1%
 
1.65751361810.1%
 
1.09110832210.1%
 
1.72781884710.1%
 
-0.24283246710.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-1.68251085310.1%
 
-1.6784828910.1%
 
-1.67776727710.1%
 
-1.67665326610.1%
 
-1.67506957110.1%
 
-1.66578364410.1%
 
-1.6647077810.1%
 
-1.65908992310.1%
 
-1.65756070610.1%
 
-1.65225493910.1%
 
ValueCountFrequency (%) 
1.74172365710.1%
 
1.73538160310.1%
 
1.73484528110.1%
 
1.72783780110.1%
 
1.72781884710.1%
 
1.7260227210.1%
 
1.72526097310.1%
 
1.71954464910.1%
 
1.71879029310.1%
 
1.71534478710.1%
 

target
Real number (ℝ)

UNIQUE

Distinct count1000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0353978709964906e-09
Minimum-2.395596742630005
Maximum2.223865270614624
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-08-25T00:26:01.315928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.395596743
5-th percentile-1.802006
Q1-0.7622636557
median0.2310956791
Q30.7920046151
95-th percentile1.310788637
Maximum2.223865271
Range4.619462013
Interquartile range (IQR)1.554268271

Descriptive statistics

Standard deviation0.9999999997
Coefficient of variation (CV)965812300.5
Kurtosis-0.7907470088
Mean1.035397871e-09
Median Absolute Deviation (MAD)0.6818552837
Skewness-0.4746445159
Sum1.035397871e-06
Variance0.9999999995
2020-08-25T00:26:01.419476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.324217349310.1%
 
0.970030605810.1%
 
0.107020057710.1%
 
-0.670610427910.1%
 
-0.107507869610.1%
 
-1.69278097210.1%
 
0.783889412910.1%
 
-1.60683202710.1%
 
1.14198422410.1%
 
-1.72911214810.1%
 
0.966892480910.1%
 
-1.27869772910.1%
 
-0.750295162210.1%
 
0.762393534210.1%
 
-1.68103432710.1%
 
-2.14331603110.1%
 
0.156899094610.1%
 
0.940146684610.1%
 
0.977213810.1%
 
-1.57271325610.1%
 
-2.17703747710.1%
 
-1.65883350410.1%
 
-1.36460125410.1%
 
-1.73960006210.1%
 
-0.934252083310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.39559674310.1%
 
-2.39254546210.1%
 
-2.35133981710.1%
 
-2.22224521610.1%
 
-2.21532177910.1%
 
-2.21301579510.1%
 
-2.18346571910.1%
 
-2.17707133310.1%
 
-2.17703747710.1%
 
-2.16482973110.1%
 
ValueCountFrequency (%) 
2.22386527110.1%
 
2.06450605410.1%
 
2.03663778310.1%
 
2.00079131110.1%
 
1.81820118410.1%
 
1.77115213910.1%
 
1.76482224510.1%
 
1.76267683510.1%
 
1.73456013210.1%
 
1.69646120110.1%
 

Interactions

2020-08-25T00:25:53.751701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:53.897673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:54.049636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:54.198622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:54.353647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:54.506836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:54.815658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:54.972170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:55.133754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:55.292535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:55.456133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:55.619016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:55.832463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:56.002074image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:56.168496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:56.326710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:56.483620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:56.638735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:56.788698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:56.945728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:57.112631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:57.272899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:57.437494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:57.599949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:57.771289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:57.931568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:58.095832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:58.254352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:58.418723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:58.583366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:58.738212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:58.883061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:59.038850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:59.187418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:59.338397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:25:59.490942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:26:01.536178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:26:01.714376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:26:01.890429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:26:02.070996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:25:59.895722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:00.086463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5target
0-0.917226-0.481465-0.907335-0.3990801.4857331.227461
1-1.090566-0.575185-0.2028530.749390-0.8198650.706417
20.8769460.247480-0.299574-0.090049-1.554089-1.486970
3-0.5099950.0922760.9488431.0008071.0603261.092014
4-0.703328-1.364161-0.773210-1.5918650.4637220.055480
50.8935500.3593541.2361661.457618-0.072889-1.183211
60.271911-0.4015820.3192790.726654-0.4636340.838452
71.4700201.5368582.6006941.5750991.6042091.764822
80.9855271.2002402.4209081.132959-1.412367-0.809036
9-0.361175-0.4711250.8084261.044495-0.2428320.932545

Last rows

oz1oz2oz3oz4oz5target
990-1.292045-1.508788-0.312728-1.099742-1.237561-0.951095
991-0.0885130.690751-1.003496-1.051901-0.192020-0.427921
9920.6665830.5687540.8323570.270107-1.393317-1.809699
9930.3140690.7726910.123986-0.3991380.195763-1.568618
994-0.845906-0.4103660.0933090.9166251.4356491.386536
9951.2511190.8258550.688708-0.048985-1.538035-1.944339
9960.4632980.532060-0.390424-0.6841860.225955-1.158087
997-1.119503-1.026188-0.2830780.6621820.4054610.428558
9981.0279460.8742491.2795051.2731410.560302-1.177858
9990.5025681.061862-0.045315-0.9598160.484668-1.959077